Fishing boat operation monitoring method and system

Abstract

The invention relates to a radio positioning technology, and provides a fishing boat operation monitoring method and system, and the method comprises the steps: obtaining the operation state data of a target fishing boat in real time, carrying out the operation abnormality analysis of the target fishing boat according to the operation state data, obtaining an abnormality analysis result, judging whether the operation of the target fishing boat is abnormal or not according to the abnormality analysis result, and if yes, stopping the operation of the target fishing boat. If yes, returning to the step of acquiring the operation state data of the target fishing boat in real time, otherwise, acquiring Beidou positioning data of the target fishing boat, acquiring distance data between a preset number of communication stations and the target fishing boat based on a radio transmission system, and performing triangulation positioning on the target fishing boat based on the distance data to obtain triangulation positioning data; the initial departure position of the target fishing boat is obtained, current position fitting is carried out according to the initial departure position and the operation state data, self-fitting position data is obtained, multi-source fusion accurate positioning is carried out according to the three kinds of positioning data, and accurate positioning data is obtained. According to the invention, the positioning accuracy of the fishing boat can be improved.

Description

Technical Field

[0001] The present invention relates to the field of radio positioning technology, and in particular to a method and system for monitoring fishing vessel operations. Background Art

[0002] As safety risks in marine operations increase, fishing vessel monitoring technology is undergoing a transformation toward intelligent and precise capabilities. Currently, fishing vessel monitoring systems primarily rely on satellite positioning, AIS (Automatic Identification System), and sensor networks for dynamic tracking, pinpointing the vessel's position when abnormal operations occur.

[0003] Existing fishing vessel positioning methods generally use global satellite positioning systems (such as GPS and Beidou) as the core positioning means, which perform well in coverage and all-weather working capabilities, and can provide basic location data support for fishery management departments.

[0004] While existing satellite positioning systems offer good positioning accuracy in specific areas, and ground-based augmentation systems can provide even higher precision, the limited transmission rates of traditional satellite communication terminals can lead to positioning data delays, impacting emergency response efficiency. Furthermore, positioning accuracy degrades in specific environments, such as those with strong electromagnetic interference, obstructions from complex terrain, and a lack of visible satellites, making it difficult to meet rescue needs when fishing vessels operate abnormally. Summary of the Invention

[0005] The present invention provides a method and system for monitoring fishing vessel operations, the main purpose of which is to solve the problem that existing fishing vessel monitoring methods cannot accurately locate fishing vessels.

[0006] To achieve the above-mentioned object, the present invention provides a method for monitoring fishing vessel operations, comprising:

[0007] Use preset sensors to obtain real-time operating status data of target fishing vessels;

[0008] performing an operation abnormality analysis on the target fishing vessel according to the operation status data to obtain an abnormality analysis result;

[0009] Determining whether the target fishing vessel is operating abnormally according to the abnormality analysis result;

[0010] If the target fishing vessel has no abnormal operation, returning to the step of obtaining the operating status data of the target fishing vessel in real time using the preset sensor;

[0011] If the target fishing vessel operates abnormally, obtaining Beidou positioning data of the target fishing vessel;

[0012] Acquiring distance data between a preset number of communication stations and the target fishing vessel based on a radio transmission system, and performing triangulation positioning on the target fishing vessel based on the distance data to obtain triangulation positioning data;

[0013] Acquiring an initial departure position of the target fishing vessel, performing current position fitting based on the initial departure position and the operation status data to obtain self-fitting position data;

[0014] Multi-source fusion precise positioning is performed based on the Beidou positioning data, the triangulation positioning data and the self-fitting position data to obtain precise positioning data.

[0015] Optionally, performing an operation abnormality analysis on the target fishing vessel according to the operation status data to obtain an abnormality analysis result includes:

[0016] Calculating the square difference of speed data in the operation status data to obtain the speed square difference;

[0017] Numerical processing is performed on the heading data in the operation status data to obtain numerical heading data,

[0018] Calculating the degree of fluctuation of the digitized heading data to obtain a heading fluctuation coefficient;

[0019] Determining whether the speed square difference is greater than a preset square difference threshold;

[0020] If the speed square difference is greater than the square difference threshold, the abnormality analysis result is determined to be an operation abnormality;

[0021] If the speed square difference is less than or equal to the square difference threshold, determining whether the heading fluctuation coefficient is greater than a preset fluctuation threshold;

[0022] If the heading fluctuation coefficient is greater than the fluctuation threshold, the abnormality analysis result is determined to be an operation abnormality;

[0023] If the heading fluctuation coefficient is less than or equal to the fluctuation threshold, the abnormality analysis result is determined to be normal operation.

[0024] Optionally, performing current position fitting according to the initial starting position and the operation status data to obtain self-fitting position data includes:

[0025] Acquiring heading data, acceleration data, and speed data included in the operation status data;

[0026] The heading data, the acceleration data, and the speed data are sampled in sequence according to a preset sampling interval to obtain a travel distance sequence, an acceleration sequence data, and a speed sequence data;

[0027] Calculating the travel distance data of each sampling interval according to the acceleration sequence data and the speed sequence data to obtain a travel distance sequence;

[0028] Drawing the travel path of the target fishing boat according to the travel distance sequence and the travel distance sequence in a preset two-dimensional space to obtain two-dimensional data of the travel path;

[0029] The final position of the target fishing boat is fitted according to the two-dimensional data of the driving path and the initial starting position to obtain self-fitting position data.

[0030] Optionally, performing multi-source fusion precise positioning according to the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain precise positioning data includes:

[0031] Performing positioning quality analysis on the Beidou positioning data to obtain a positioning quality coefficient;

[0032] Determining whether the positioning quality coefficient is greater than a preset positioning quality threshold;

[0033] If the positioning quality coefficient is less than or equal to the positioning quality threshold;

[0034] If the positioning quality coefficient is greater than the positioning quality threshold, performing differential repair on the Beidou positioning data;

[0035] Determine a target triangulated area according to the Beidou positioning data, the triangulated positioning data, and the self-fitting position data;

[0036] Acquire angle data of the target triangular area;

[0037] Dynamically calculate the weights of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain a weight parameter set

[0038] Calculate the distance ratio parameters of the Beidou positioning data, the triangulation positioning data and the self-fitting position data according to the weight parameter set;

[0039] Determining whether the maximum angle in the angle data is greater than a preset angle threshold;

[0040] If the maximum angle is greater than the angle threshold, solving the precise positioning according to the position data corresponding to the angle data except the maximum angle in the angle data to obtain precise positioning data;

[0041] If the maximum angle is less than or equal to the angle threshold, the unique position point in the target triangular area is confirmed according to the distance ratio parameter to obtain precise positioning data.

[0042] Optionally, performing positioning quality analysis on the Beidou positioning data to obtain a positioning quality coefficient includes:

[0043] Get the current Beidou positioning signal-to-noise ratio, number of visible satellites, horizontal precision coefficient, and positioning solution type;

[0044] The positioning quality coefficient is calculated using the following formula based on the signal-to-noise ratio, the number of visible satellites, the horizontal dilution of precision, and the positioning solution type:

[0045]

[0046] Wherein, Q is the positioning quality coefficient, Q max is the preset maximum positioning quality coefficient, SNR is the signal-to-noise ratio, N is the number of visible satellites, HDOP is the horizontal dilution of precision, and Type is the positioning solution type value.

[0047] Optionally, the dynamically calculating weights of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain a weight parameter set includes:

[0048] Calculate the weight parameter of the Beidou positioning data according to the positioning quality coefficient and a preset proportional coefficient to obtain the Beidou positioning weight;

[0049] Calculate the remaining weight according to the Beidou positioning weight;

[0050] The weight parameters of the triangulated positioning data and the self-fitting position data are calculated according to preset ratio parameters of the triangulated positioning data and the self-fitting position data and the residual weight to obtain the triangulated positioning weight and the self-fitting weight.

[0051] Optionally, the calculating, according to the weight parameter set, the distance ratio parameters of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data includes:

[0052] The distance ratio parameter is calculated using the following formula:

[0053]

[0054] Among them, A is the Beidou positioning weight included in the weight parameter set, B is the triangulation positioning weight included in the weight parameter set, C is the self-fitting weight included in the weight parameter set, L A L is the ratio parameter corresponding to the Beidou positioning weight in the distance ratio parameter, B L is the ratio parameter corresponding to the triangulation positioning weight in the distance ratio parameter, C is the proportional parameter corresponding to the self-fitting weight in the distance proportional parameter.

[0055] Optionally, solving the precise positioning according to the position data corresponding to the angle data excluding the maximum angle in the angle data to obtain the precise positioning data includes:

[0056] Confirming the positioning data corresponding to the non-maximum angle in the angle data to obtain a first positioning point and a second positioning point;

[0057] determining a distance ratio between the first positioning point and the second positioning point according to the distance ratio parameter;

[0058] A positioning point on the line connecting the first positioning point and the second positioning point is determined according to the distance ratio to obtain precise positioning data.

[0059] Optionally, the determining a unique position point in the target triangular area according to the distance ratio parameter to obtain precise positioning data includes:

[0060] Confirm that, in the target triangulated area, the vertex corresponding to the Beidou positioning data is the first vertex, the vertex corresponding to the triangulated positioning data is the second vertex, and the vertex corresponding to the self-fitting position data is the third vertex;

[0061] According to the distance ratio parameter, respectively determine the distance ratio parameters corresponding to the first vertex, the second vertex, and the third vertex to obtain a first ratio parameter, a second ratio parameter, and a third ratio parameter;

[0062] Constructing an Apollonia circle of the first vertex and the second vertex according to the first scale parameter and the second scale parameter to obtain a first Apollonia circle;

[0063] Constructing the Apollonia circle of the second vertex and the third vertex according to the second scale parameter and the third scale parameter to obtain a second Apollonia circle;

[0064] The position data of the intersection of the first Avalokitesvara circle and the second Avalokitesvara circle in the target triangular area are obtained to obtain precise positioning data.

[0065] In order to solve the above problems, the present invention also provides a fishing vessel operation monitoring system, which includes an abnormality analysis module, an abnormality judgment module, a positioning acquisition module, and a fusion positioning module, wherein:

[0066] The abnormality analysis module is used to obtain the operating status data of the target fishing vessel in real time using a preset sensor, and perform an abnormality analysis on the target fishing vessel based on the operating status data to obtain an abnormality analysis result;

[0067] The abnormality judgment module is used to judge whether the target fishing vessel has an abnormal operation based on the abnormality analysis result; if the target fishing vessel has no abnormal operation, return to the step of using the preset sensor to obtain the target fishing vessel's operating status data in real time; if the target fishing vessel has an abnormal operation, obtain the Beidou positioning data of the target fishing vessel;

[0068] The positioning acquisition module is configured to acquire distance data between a preset number of communication stations and the target fishing vessel based on a radio transmission system, perform triangulation positioning on the target fishing vessel based on the distance data to obtain triangulation positioning data, obtain an initial departure position of the target fishing vessel, and perform current position fitting based on the initial departure position and the operation status data to obtain self-fitting position data;

[0069] The fusion positioning module is used to perform multi-source fusion precise positioning based on the Beidou positioning data, the triangulation positioning data and the self-fitting position data to obtain precise positioning data.

[0070] The embodiment of the present invention obtains the operating status data of the target fishing vessel in real time through a preset sensor, and combines the dual indicator analysis of the speed square difference and the heading fluctuation coefficient to effectively identify the abnormal operating status of the fishing vessel, thereby improving the accuracy and reliability of anomaly detection; obtains the Beidou positioning data, triangulation positioning data and self-fitting position data of the target fishing vessel based on the radio transmission system, and delineates the triangular area to be positioned according to the Beidou positioning data, triangulation positioning data and self-fitting position data; adjusts the positioning strategy by judging the angle data of the target triangulation area to ensure positioning stability in complex environments, dynamically allocates data weights based on the quality coefficient of the Beidou positioning data, solves the positioning point in combination with the Apollonia circle geometry principle, objectively quantifies the reliability of different positioning data, and further improves the positioning accuracy. The present invention does not need to rely on high-precision special equipment, avoids positioning errors caused by signal blocking, and significantly improves the efficiency and positioning accuracy of fishing vessel operation monitoring through full-process automated processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic flow chart of a method for monitoring fishing vessel operations according to an embodiment of the present invention;

[0072] Figure 2 This is a functional module diagram of a fishing vessel operation monitoring system provided by one embodiment of the present invention.

[0073] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0074] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] The embodiment of the present application provides a method for monitoring the operation of a fishing vessel. The execution subject of the method for monitoring the operation of a fishing vessel includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for monitoring the operation of a fishing vessel can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0076] Reference Figure 1 FIG. 1 is a flow chart of a method for monitoring fishing vessel operations according to an embodiment of the present invention. In this embodiment, the method for monitoring fishing vessel operations includes:

[0077] S1. Use preset sensors to obtain the operating status data of the target fishing vessel in real time.

[0078] In an embodiment of the present invention, the operation status data includes speed data, heading data, acceleration data, hull tilt data, ambient wind speed data, and ambient wind direction data of the target fishing vessel.

[0079] In detail, by installing speed sensors on the bottom of the hull or near the propulsion system, the Doppler effect or the principle of hydrodynamics is used to monitor the speed of the target fishing boat in the water in real time.

[0080] In detail, with the help of a high-precision heading sensor, such as an electronic compass or a global navigation satellite system (GNSS), the sailing direction of the fishing vessel is determined in real time to obtain heading data.

[0081] Specifically, acceleration sensors are installed at the center of gravity or key structures of the hull to detect the acceleration changes of the fishing boat during navigation.

[0082] Specifically, by arranging an inclination sensor on the horizontal reference plane of the hull, the inclination angle of the hull relative to the horizontal plane is monitored in real time.

[0083] Specifically, by installing a wind speed sensor on the mast or at a high place of the fishing boat, the wind speed of the surrounding environment is measured in real time using principles such as ultrasonic waves, propellers or hot wires.

[0084] S2. Performing an operation abnormality analysis on the target fishing vessel according to the operation status data to obtain an abnormality analysis result.

[0085] In an embodiment of the present invention, the operation abnormality analysis of the target fishing vessel is performed based on the operation status data to obtain the abnormality analysis result. This is to achieve abnormality detection by quantitatively analyzing the operation status data of the target fishing vessel. The square difference of the speed data is first calculated to obtain the speed fluctuation degree, and the fluctuation coefficient is calculated after numerical processing of the heading data. Then, a hierarchical judgment logic is adopted, that is, firstly judging whether the square difference of the speed exceeds a preset threshold. If it exceeds, the operation is judged to be abnormal. If it does not exceed, it is further judged whether the heading fluctuation coefficient exceeds the preset threshold. If it exceeds, it is judged to be abnormal. If it does not exceed, it is judged to be normal. In this way, the abnormal identification of the operation status of the fishing vessel is achieved by the hierarchical threshold judgment of the dual indicators, providing a quantitative basis for operation monitoring and risk warning.

[0086] In an embodiment of the present invention, performing an operation abnormality analysis on the target fishing vessel according to the operation status data to obtain an abnormality analysis result includes:

[0087] Calculating the square difference of speed data in the operation status data to obtain the speed square difference;

[0088] Numerical processing is performed on the heading data in the operation status data to obtain numerical heading data,

[0089] Calculating the degree of fluctuation of the digitized heading data to obtain a heading fluctuation coefficient;

[0090] Determining whether the speed square difference is greater than a preset square difference threshold;

[0091] If the speed square difference is greater than the square difference threshold, the abnormality analysis result is determined to be an operation abnormality;

[0092] If the speed square difference is less than or equal to the square difference threshold, determining whether the heading fluctuation coefficient is greater than a preset fluctuation threshold;

[0093] If the heading fluctuation coefficient is greater than the fluctuation threshold, the abnormality analysis result is determined to be an operation abnormality;

[0094] If the heading fluctuation coefficient is less than or equal to the fluctuation threshold, the abnormality analysis result is determined to be normal operation.

[0095] In the embodiment of the present invention, the calculation of the square difference of the speed data in the operation status data can reflect the degree of fluctuation of the speed change of the fishing boat.

[0096] In the embodiment of the present invention, the calculation of the fluctuation degree of the digitized heading data is to convert the digitized heading data into a complex number on a unit circle and then calculate the standard deviation thereof.

[0097] S3. Determine whether the target fishing vessel is operating abnormally based on the abnormality analysis result.

[0098] If the target fishing vessel has no abnormal operation, the process returns to S1, the step of obtaining the operating status data of the target fishing vessel in real time by using the preset sensor.

[0099] In an embodiment of the present invention, when the target fishing vessel does not have any abnormal operation, the step of obtaining the target fishing vessel's operating status data in real time using a preset sensor is re-executed to achieve cyclic monitoring of abnormal operation of the fishing vessel.

[0100] If the target fishing boat is operating abnormally, execute S4 to obtain Beidou positioning data of the target fishing boat.

[0101] In an embodiment of the present invention, the Beidou positioning data of the target fishing boat is obtained by using a preset Beidou receiver on the target fishing boat to receive the Beidou positioning data. After receiving the Beidou positioning data, the Beidou positioning data is sent to a preset data analysis server based on a radio transmission system.

[0102] S5. Obtain distance data between a preset number of communication stations and the target fishing boat based on a radio transmission system, and perform triangulation positioning on the target fishing boat based on the distance data to obtain triangulation positioning data.

[0103] In an embodiment of the present invention, the method of obtaining distance data between a preset number of communication stations and the target fishing boat based on a radio transmission system, performing triangulation positioning on the target fishing boat based on the distance data, and obtaining triangulation positioning data means that after the distance data between a preset number of communication stations and the target fishing boat are sent to a preset data analysis server based on the radio transmission system, triangulation positioning is performed in the data analysis server according to the received distance data.

[0104] In this embodiment of the present invention, the radio transmission system utilizes electromagnetic waves to transmit information in free space and is widely used in broadcasting, communications, navigation, remote sensing, and other fields. Its core function is to wirelessly transmit information through the emission, propagation, and reception of electromagnetic signals, eliminating the need for physical cable connections. This system offers flexibility, convenience, and wide coverage, enabling data exchange and transmission between fishing vessels and shore-based communication stations.

[0105] In the embodiment of the present invention, the preset number of communication stations may refer to two communication stations.

[0106] In an embodiment of the present invention, triangulating the target fishing vessel based on the distance data to obtain triangulated positioning data refers to obtaining the distance between two communication stations, calculating the angle data of the triangle formed by the two communication stations and the target fishing boat based on the distance between the two communication stations and the distances of the two communication stations from the target fishing boat, and calculating the triangulated positioning data of the target fishing boat based on the positions of the two communication stations based on trigonometric functions.

[0107] In an embodiment of the present invention, by obtaining distance data between a preset number of communication stations and the target fishing boat based on a radio transmission system, triangulating the target fishing boat based on the distance data to obtain triangulated positioning data, the efficiency of subsequent calculation of precise positioning data can be improved.

[0108] S6. Acquire the initial departure position of the target fishing vessel, perform current position fitting based on the initial departure position and the operation status data, and obtain self-fitting position data.

[0109] In an embodiment of the present invention, after the current position is fitted according to the initial starting position and the operation status data to obtain self-fitting position data, the self-fitting position data is sent to a preset data analysis server based on a radio transmission system.

[0110] In an embodiment of the present invention, the current position fitting is performed based on the initial departure position and the operation status data to obtain the self-fitting position data, which means that the travel path is fitted in real time based on the speed and heading of the fishing boat in each divided time period.

[0111] In an embodiment of the present invention, performing current position fitting based on the initial starting position and the operation status data to obtain self-fitting position data includes:

[0112] Acquiring heading data, acceleration data, and speed data included in the operation status data;

[0113] The heading data, the acceleration data, and the speed data are sampled in sequence according to a preset sampling interval to obtain a travel distance sequence, an acceleration sequence data, and a speed sequence data;

[0114] Calculating the travel distance data of each sampling interval according to the acceleration sequence data and the speed sequence data to obtain a travel distance sequence;

[0115] Drawing the travel path of the target fishing boat according to the travel distance sequence and the travel distance sequence in a preset two-dimensional space to obtain two-dimensional data of the travel path;

[0116] The final position of the target fishing boat is fitted according to the two-dimensional data of the driving path and the initial starting position to obtain self-fitting position data.

[0117] In an embodiment of the present invention, by obtaining the initial departure position of the target fishing boat, performing current position fitting based on the initial departure position and the operation status data, and obtaining self-fitting position data, the efficiency of subsequent calculation of precise positioning data can be improved.

[0118] S7. Perform multi-source fusion precise positioning according to the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain precise positioning data.

[0119] In an embodiment of the present invention, the multi-source fusion precise positioning based on the Beidou positioning data, the triangulation positioning data and the self-fitting position data refers to the multi-source fusion precise positioning based on the Beidou positioning data, the triangulation positioning data and the self-fitting position data in a preset data analysis server.

[0120] In an embodiment of the present invention, the multi-source fusion precise positioning is performed based on the Beidou positioning data, the triangulation positioning data and the self-fitting position data to obtain precise positioning data. This is achieved by first performing a quality analysis on the Beidou positioning data to obtain a positioning quality coefficient, and if the coefficient is greater than a preset threshold, performing differential repair on it; then, based on the three types of data, the target triangulation area is confirmed and its angle data is obtained, and the weight parameter set and distance ratio parameter of the three are dynamically calculated; finally, depending on whether the maximum angle of the target triangulation area exceeds the preset threshold, the non-maximum angle corresponding position data is used to solve or the unique position point is determined by the distance ratio parameter, so as to finally obtain precise positioning data.

[0121] In an embodiment of the present invention, performing multi-source fusion precise positioning based on the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain precise positioning data includes:

[0122] Performing positioning quality analysis on the Beidou positioning data to obtain a positioning quality coefficient;

[0123] Determining whether the positioning quality coefficient is greater than a preset positioning quality threshold;

[0124] If the positioning quality coefficient is less than or equal to the positioning quality threshold;

[0125] If the positioning quality coefficient is greater than the positioning quality threshold, performing differential repair on the Beidou positioning data;

[0126] Determine a target triangulated area according to the Beidou positioning data, the triangulated positioning data, and the self-fitting position data;

[0127] Acquire angle data of the target triangular area;

[0128] Dynamically calculate the weights of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain a weight parameter set

[0129] Calculate the distance ratio parameters of the Beidou positioning data, the triangulation positioning data and the self-fitting position data according to the weight parameter set;

[0130] Determining whether the maximum angle in the angle data is greater than a preset angle threshold;

[0131] If the maximum angle is greater than the angle threshold, solving the precise positioning according to the position data corresponding to the angle data except the maximum angle in the angle data to obtain precise positioning data;

[0132] If the maximum angle is less than or equal to the angle threshold, the unique position point in the target triangular area is confirmed according to the distance ratio parameter to obtain precise positioning data.

[0133] In the embodiment of the present invention, the differential repair of the Beidou positioning data is performed by sending a positioning error correction value through a base station, and the fishing boat corrects the original Beidou data after receiving it.

[0134] In an embodiment of the present invention, the preset angle threshold may be 90 degrees.

[0135] In an embodiment of the present invention, performing positioning quality analysis on the Beidou positioning data to obtain a positioning quality coefficient includes:

[0136] Get the current Beidou positioning signal-to-noise ratio, number of visible satellites, horizontal precision coefficient, and positioning solution type;

[0137] The positioning quality coefficient is calculated using the following formula based on the signal-to-noise ratio, the number of visible satellites, the horizontal dilution of precision, and the positioning solution type:

[0138]

[0139] Wherein, Q is the positioning quality coefficient, Q max is the preset maximum positioning quality coefficient, SNR is the signal-to-noise ratio, N is the number of visible satellites, HDOP is the horizontal dilution of precision, and Type is the positioning solution type value.

[0140] In detail, the positioning solution type value refers to the corresponding value according to different positioning solution types. When the positioning solution type is differential positioning, the value is 1, and when the positioning solution type is single-point positioning, the value is 0.7.

[0141] In detail, the signal-to-noise ratio, number of visible satellites, horizontal precision coefficient, and positioning solution type of the current Beidou positioning can be obtained through a Beidou receiver.

[0142] Specifically, the signal-to-noise ratio (SNR) reflects the clarity and anti-interference capability of satellite signals. The number of visible satellites refers to the number of Beidou satellites that the receiver can track and use for positioning. A greater number of satellites indicates a better geometric distribution and higher positioning reliability. The horizontal dilution of precision (HDOP) reflects the impact of satellite geometric distribution on horizontal positioning accuracy. Lower HDOP values (typically ≤1 is optimal) indicate lower positioning errors.

[0143] In detail, the horizontal precision factor reflects the impact of satellite geometric distribution on horizontal positioning error. The smaller the value (usually <1), the higher the accuracy. Therefore, in the above formula for calculating the positioning quality coefficient, the horizontal precision factor is in the denominator. The smaller the horizontal precision factor is, the higher the final positioning quality coefficient is.

[0144] In detail, in the above formula for calculating the positioning quality coefficient, dividing the signal-to-noise ratio by 45 is a normalization process; in max(N-4,0), N-4 represents the redundancy beyond the minimum requirement (4 satellites), which enhances positioning reliability. When N-4 is less than 4, it is zero, reflecting that positioning is unreliable when there are insufficient satellites; the maximum positioning quality coefficient is used as a normalization coefficient to normalize the final calculation result of the numerator so that the final result is between 0 and 1.

[0145] In an embodiment of the present invention, confirming the target triangular area based on the Beidou positioning data, the triangulated positioning data and the self-fitting position data refers to taking the positioning points corresponding to the Beidou positioning data, the triangulated positioning data and the self-fitting position data as vertices, and delineating a triangular area according to the three vertices.

[0146] In an embodiment of the present invention, the dynamically calculating the weights of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain a weight parameter set includes:

[0147] Calculate the weight parameter of the Beidou positioning data according to the positioning quality coefficient and a preset proportional coefficient to obtain the Beidou positioning weight;

[0148] Calculate the remaining weight according to the Beidou positioning weight;

[0149] The weight parameters of the triangulated positioning data and the self-fitting position data are calculated according to preset ratio parameters of the triangulated positioning data and the self-fitting position data and the residual weight to obtain the triangulated positioning weight and the self-fitting weight.

[0150] In detail, the weight parameter of the Beidou positioning data is calculated according to the positioning quality coefficient and the preset proportional coefficient, which means calculating the ratio of the positioning quality coefficient to the preset maximum positioning quality coefficient, and then multiplying it by the preset proportional coefficient to obtain the Beidou positioning weight. For example, the positioning quality coefficient is 0.7, the maximum positioning quality coefficient is 1, and the preset proportional coefficient is 0.6, then the Beidou positioning weight is (0.7 / 1)*0.6=0.42.

[0151] In detail, the calculation of the residual weight based on the Beidou positioning weight means that the value obtained by subtracting the Beidou positioning weight from 1 is the residual weight. For example, if the positioning quality coefficient is 0.42, the residual weight is 0.48.

[0152] Specifically, in calculating the weight parameters of the triangulated positioning data and the self-fitting position data based on a preset ratio parameter of the triangulated positioning data and the self-fitting position data and the residual weight, the triangulated positioning weight and the self-fitting weight obtained may include the preset ratio parameter being 1, i.e., the preset ratio of the triangulated positioning data to the self-fitting position data is 1:1. For example, when the residual weight is 0.48, the triangulated positioning weight and the self-fitting weight are both 0.24.

[0153] In an embodiment of the present invention, the calculating, according to the weight parameter set, the distance ratio parameters of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data includes:

[0154] The distance ratio parameter is calculated using the following formula:

[0155]

[0156] Among them, A is the Beidou positioning weight included in the weight parameter set, B is the triangulation positioning weight included in the weight parameter set, C is the self-fitting weight included in the weight parameter set, L A L is the ratio parameter corresponding to the Beidou positioning weight in the distance ratio parameter, B L is the ratio parameter corresponding to the triangulation positioning weight in the distance ratio parameter, C is the proportional parameter corresponding to the self-fitting weight in the distance proportional parameter.

[0157] In detail, in the formula for calculating the distance ratio parameter, the new ratio parameter is recalculated by calculating the inverse of each weight parameter in the weight parameter set. That is, the larger the weight parameter, the smaller the corresponding distance ratio parameter, and the smaller the weight parameter, the larger the corresponding distance ratio parameter. For example, when calculating the ratio parameter corresponding to the Beidou positioning weight, the sum of the inverses of the Beidou positioning weight, the triangulation positioning weight, and the self-fitting weight is first calculated, and then the ratio of the Beidou positioning weight to the sum of the inverses of the three weights is calculated. The result is the ratio parameter corresponding to the Beidou positioning weight.

[0158] In the above formula for calculating the distance ratio parameter, by taking the inverse of the Beidou positioning weight, the triangulation positioning weight, and the self-fitting weight for calculation, the logical relationship that the greater the weight, the smaller the distance ratio parameter can be reflected.

[0159] In an embodiment of the present invention, solving the precise positioning based on the position data corresponding to the angle data excluding the maximum angle in the angle data to obtain the precise positioning data includes:

[0160] Confirming the positioning data corresponding to the non-maximum angle in the angle data to obtain a first positioning point and a second positioning point;

[0161] determining a distance ratio between the first positioning point and the second positioning point according to the distance ratio parameter;

[0162] A positioning point on the line connecting the first positioning point and the second positioning point is determined according to the distance ratio to obtain precise positioning data.

[0163] Specifically, determining the positioning data corresponding to the non-maximum angle in the angle data to obtain the first and second positioning points refers to determining the positioning data corresponding to the two angles in the angle data excluding the maximum angle in the three positioning data. For example, if the vertex of the total maximum angle in the triangular area corresponds to the self-fitting positioning data, the first positioning point can be Beidou positioning data and the second positioning point can be triangulation positioning data, or the second positioning point can be Beidou positioning data and the first positioning point can be triangulation positioning data.

[0164] In detail, confirming the distance ratio between the first positioning point and the second positioning point based on the distance ratio parameter refers to obtaining the ratio parameters corresponding to the positioning data of the real-time first positioning point and the second positioning point in the distance ratio parameter. For example, when the second positioning point corresponds to triangulation positioning data, or the second positioning point corresponds to Beidou positioning data, then the ratio parameters corresponding to the triangulation positioning data and the Beidou positioning data in the distance ratio parameter are obtained.

[0165] In detail, determining the distance ratio between the first positioning point and the second positioning point according to the distance ratio parameter may be comparing the ratio parameter corresponding to the first positioning point with the ratio parameter corresponding to the second positioning point.

[0166] In detail, confirming a positioning point on the line connecting the first positioning point and the second positioning point according to the distance ratio to obtain precise positioning data refers to confirming a point on the line connecting the first positioning point and the second positioning point that meets the distance ratio.

[0167] In the embodiment of the present invention, the step of determining a unique location point in the target triangular area according to the distance ratio parameter to obtain precise positioning data includes:

[0168] Confirm that, in the target triangulated area, the vertex corresponding to the Beidou positioning data is the first vertex, the vertex corresponding to the triangulated positioning data is the second vertex, and the vertex corresponding to the self-fitting position data is the third vertex;

[0169] According to the distance ratio parameter, respectively determine the distance ratio parameters corresponding to the first vertex, the second vertex, and the third vertex to obtain a first ratio parameter, a second ratio parameter, and a third ratio parameter;

[0170] Constructing an Apollonia circle of the first vertex and the second vertex according to the first scale parameter and the second scale parameter to obtain a first Apollonia circle;

[0171] Constructing the Apollonia circle of the second vertex and the third vertex according to the second scale parameter and the third scale parameter to obtain a second Apollonia circle;

[0172] The position data of the intersection of the first Avalokitesvara circle and the second Avalokitesvara circle in the target triangular area are obtained to obtain precise positioning data.

[0173] In the embodiments of the present invention, the Apollonia circle is an important type of trajectory curve in plane geometry. It is defined as follows: given two fixed points A and B on a plane, the locus of all points P whose distances to the two fixed points are in a constant ratio k (k>0 and k=1) is a circle, called an Apollonia circle.

[0174] like Figure 2 FIG. 1 is a functional module diagram of a fishing vessel operation monitoring system provided by an embodiment of the present invention.

[0175] The fishing vessel operation monitoring system 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the fishing vessel operation monitoring system 100 may include an anomaly analysis module 101, an anomaly determination module 102, a positioning acquisition module 103, and a fusion positioning module 104. A module, also referred to as a unit, refers to a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These are stored in the electronic device's memory.

[0176] In this embodiment, the functions of each module / unit are as follows:

[0177] The abnormality analysis module 101 is used to obtain the operating status data of the target fishing vessel in real time using a preset sensor, and perform an abnormality analysis on the target fishing vessel based on the operating status data to obtain an abnormality analysis result;

[0178] The abnormality judgment module 102 is used to judge whether the target fishing vessel has an abnormal operation based on the abnormality analysis result. If the target fishing vessel has no abnormal operation, the module returns to the step of obtaining the target fishing vessel's operating status data in real time using a preset sensor. If the target fishing vessel has an abnormal operation, the module obtains the Beidou positioning data of the target fishing vessel.

[0179] The positioning acquisition module 103 is configured to obtain distance data between a preset number of communication stations and the target fishing vessel based on a radio transmission system, perform triangulation positioning on the target fishing vessel based on the distance data to obtain triangulation positioning data, obtain an initial departure position of the target fishing vessel, and perform current position fitting based on the initial departure position and the operation status data to obtain self-fitting position data;

[0180] The fusion positioning module 104 is used to perform multi-source fusion precise positioning according to the Beidou positioning data, the triangulation positioning data and the self-fitting position data to obtain precise positioning data.

[0181] In detail, each module in the fishing vessel operation monitoring system 100 according to the embodiment of the present invention is used in the same manner as above. Figure 1 The technical means are the same as the fishing vessel operation monitoring method described in and can produce the same technical effects, so they will not be repeated here.

[0182] In the embodiments provided herein, it should be understood that the disclosed devices, systems, and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the module division is merely a logical functional division, and actual implementation may employ other division methods.

[0183] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0184] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0185] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0186] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0187] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0188] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems recited in a system claim may also be implemented by a single unit or system through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for monitoring fishing vessel operations, characterized in that: The method comprises: Use preset sensors to obtain real-time operating status data of target fishing vessels; performing an operation abnormality analysis on the target fishing vessel according to the operation status data to obtain an abnormality analysis result; Determining whether the target fishing vessel is operating abnormally according to the abnormality analysis result; If the target fishing vessel has no abnormal operation, returning to the step of obtaining the operating status data of the target fishing vessel in real time using the preset sensor; If the target fishing vessel operates abnormally, obtaining Beidou positioning data of the target fishing vessel; Acquiring distance data between a preset number of communication stations and the target fishing vessel based on a radio transmission system, and performing triangulation positioning on the target fishing vessel based on the distance data to obtain triangulation positioning data; Acquiring an initial departure position of the target fishing vessel, performing current position fitting based on the initial departure position and the operation status data to obtain self-fitting position data; Multi-source fusion precise positioning is performed based on the Beidou positioning data, the triangulation positioning data and the self-fitting position data to obtain precise positioning data.

2. The fishing vessel operation monitoring method according to claim 1, wherein: The performing an operation abnormality analysis on the target fishing vessel according to the operation status data to obtain an abnormality analysis result includes: Calculating the square difference of speed data in the operation status data to obtain the speed square difference; Numerical processing is performed on the heading data in the operation status data to obtain numerical heading data, Calculating the degree of fluctuation of the digitized heading data to obtain a heading fluctuation coefficient; Determining whether the speed square difference is greater than a preset square difference threshold; If the speed square difference is greater than the square difference threshold, the abnormality analysis result is determined to be an operation abnormality; If the speed square difference is less than or equal to the square difference threshold, determining whether the heading fluctuation coefficient is greater than a preset fluctuation threshold; If the heading fluctuation coefficient is greater than the fluctuation threshold, the abnormality analysis result is determined to be an operation abnormality; If the heading fluctuation coefficient is less than or equal to the fluctuation threshold, the abnormality analysis result is determined to be normal operation.

3. The fishing vessel operation monitoring method according to claim 1, wherein: The performing current position fitting according to the initial starting position and the operation status data to obtain self-fitting position data includes: Acquiring heading data, acceleration data, and speed data included in the operation status data; The heading data, the acceleration data, and the speed data are sequentially sampled according to a preset sampling interval to obtain a travel distance sequence, an acceleration sequence data, and a speed sequence data; Calculating the travel distance data of each sampling interval according to the acceleration sequence data and the speed sequence data to obtain a travel distance sequence; Drawing the travel path of the target fishing boat according to the travel distance sequence and the travel distance sequence in a preset two-dimensional space to obtain two-dimensional data of the travel path; The final position of the target fishing boat is fitted according to the two-dimensional data of the driving path and the initial starting position to obtain self-fitting position data.

4. The fishing vessel operation monitoring method according to claim 1, wherein: The performing multi-source fusion precise positioning according to the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain precise positioning data includes: Performing positioning quality analysis on the Beidou positioning data to obtain a positioning quality coefficient; Determining whether the positioning quality coefficient is greater than a preset positioning quality threshold; If the positioning quality coefficient is less than or equal to the positioning quality threshold; If the positioning quality coefficient is greater than the positioning quality threshold, performing differential repair on the Beidou positioning data; Determine a target triangulated area according to the Beidou positioning data, the triangulated positioning data, and the self-fitting position data; Acquire angle data of the target triangular area; Dynamically calculate the weights of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain a weight parameter set Calculate the distance ratio parameters of the Beidou positioning data, the triangulation positioning data and the self-fitting position data according to the weight parameter set; Determining whether the maximum angle in the angle data is greater than a preset angle threshold; If the maximum angle is greater than the angle threshold, solving the precise positioning according to the position data corresponding to the angle data except the maximum angle in the angle data to obtain precise positioning data; If the maximum angle is less than or equal to the angle threshold, the unique position point in the target triangular area is confirmed according to the distance ratio parameter to obtain precise positioning data.

5. The fishing vessel operation monitoring method according to claim 4, characterized in that: The performing positioning quality analysis on the Beidou positioning data to obtain a positioning quality coefficient includes: Get the current Beidou positioning signal-to-noise ratio, number of visible satellites, horizontal precision coefficient, and positioning solution type; The positioning quality coefficient is calculated using the following formula based on the signal-to-noise ratio, the number of visible satellites, the horizontal dilution of precision, and the positioning solution type: Wherein, Q is the positioning quality coefficient, Q max is the preset maximum positioning quality coefficient, SNR is the signal-to-noise ratio, N is the number of visible satellites, HDOP is the horizontal dilution of precision, and Type is the positioning solution type value.

6. The fishing vessel operation monitoring method according to claim 4, characterized in that: The dynamically calculating weights of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data to obtain a weight parameter set includes: Calculate the weight parameter of the Beidou positioning data according to the positioning quality coefficient and a preset proportional coefficient to obtain the Beidou positioning weight; Calculate the remaining weight according to the Beidou positioning weight; The weight parameters of the triangulated positioning data and the self-fitting position data are calculated according to preset ratio parameters of the triangulated positioning data and the self-fitting position data and the residual weight to obtain the triangulated positioning weight and the self-fitting weight.

7. The fishing vessel operation monitoring method according to claim 4, wherein: The calculating, according to the weight parameter set, the distance ratio parameters of the Beidou positioning data, the triangulation positioning data, and the self-fitting position data includes: The distance ratio parameter is calculated using the following formula: Among them, A is the Beidou positioning weight included in the weight parameter set, B is the triangulation positioning weight included in the weight parameter set, C is the self-fitting weight included in the weight parameter set, L A L is the ratio parameter corresponding to the Beidou positioning weight in the distance ratio parameter, B L is the ratio parameter corresponding to the triangulation positioning weight in the distance ratio parameter, C is the proportional parameter corresponding to the self-fitting weight in the distance proportional parameter.

8. The fishing vessel operation monitoring method according to claim 4, wherein: The step of solving the precise positioning based on the position data corresponding to the angle data except the maximum angle in the angle data to obtain the precise positioning data includes: Confirming the positioning data corresponding to the non-maximum angle in the angle data to obtain a first positioning point and a second positioning point; determining a distance ratio between the first positioning point and the second positioning point according to the distance ratio parameter; A positioning point on the line connecting the first positioning point and the second positioning point is determined according to the distance ratio to obtain precise positioning data.

9. The fishing vessel operation monitoring method according to claim 4, wherein: The step of confirming a unique position point in the target triangular area according to the distance ratio parameter to obtain precise positioning data includes: Confirm that, in the target triangulated area, the vertex corresponding to the Beidou positioning data is the first vertex, the vertex corresponding to the triangulated positioning data is the second vertex, and the vertex corresponding to the self-fitting position data is the third vertex; According to the distance ratio parameter, respectively determine the distance ratio parameters corresponding to the first vertex, the second vertex, and the third vertex to obtain a first ratio parameter, a second ratio parameter, and a third ratio parameter; Constructing an Apollonia circle of the first vertex and the second vertex according to the first scale parameter and the second scale parameter to obtain a first Apollonia circle; Constructing the Apollonia circle of the second vertex and the third vertex according to the second scale parameter and the third scale parameter to obtain a second Apollonia circle; The position data of the intersection of the first Avalokitesvara circle and the second Avalokitesvara circle in the target triangular area are obtained to obtain precise positioning data.

10. A fishing vessel operation monitoring system, characterized in that: The system includes an abnormality analysis module, an abnormality judgment module, a positioning acquisition module, and a fusion positioning module, wherein: The abnormality analysis module is used to obtain the operating status data of the target fishing vessel in real time using a preset sensor, and perform an abnormality analysis on the target fishing vessel based on the operating status data to obtain an abnormality analysis result; The abnormality judgment module is used to judge whether the target fishing vessel has an abnormal operation based on the abnormality analysis result; if the target fishing vessel has no abnormal operation, return to the step of using the preset sensor to obtain the target fishing vessel's operating status data in real time; if the target fishing vessel has an abnormal operation, obtain the Beidou positioning data of the target fishing vessel; The positioning acquisition module is configured to acquire distance data between a preset number of communication stations and the target fishing vessel based on a radio transmission system, perform triangulation positioning on the target fishing vessel based on the distance data to obtain triangulation positioning data, obtain an initial departure position of the target fishing vessel, and perform current position fitting based on the initial departure position and the operation status data to obtain self-fitting position data; The fusion positioning module is used to perform multi-source fusion precise positioning based on the Beidou positioning data, the triangulation positioning data and the self-fitting position data to obtain precise positioning data.

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